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Modernising your data warehouse in 24 hours

Webinar

Europe

Join Databricks, Datalytyx and Talend for this free live webinar on ‘Modernising your data warehouse in 24 hours’ to see how easy it is to get your data-focussed initiatives over the technical barriers and into reality. In this webinar you'll see how to: Quickly spin up cloud-based technology, enable business users to integrate, cleanse and master data, automatically scale storage and compute resources, deliver it all with no set up fee – just a monthly subscription cost with no lock-in

Unified Analytics | Genomics Hands-on lab

Regional Event

Cambridge, UK

In this workshop, we’ll walkthrough how the Databricks Unified Analytics Platform for Genomics simplifies the end-to-end process of turning raw sequencing data into actionable insights at scale. Introduced by the original creators of Apache Spark, this platform makes it simple to deploy Spark-based bioinformatics tools on cloud computing, and rapidly accelerates common genomic analyses. Join this half day technical workshop to learn how to: - Call variants, both in a single sample and across multiple samples, using our accelerated GATK4 pipelines - Use Spark SQL to characterise the association of variants in a population with phenotypes - Use machine learning to model genome-wide disease risk across multiple variants associated with a phenotype of interest

London Apache Spark Meetup: The Road to Upcoming Apache Spark 3.0, Koalas and Neptune Spark Meetup

Meetup

London, England

Join us for the next Apache Spark London Meetup! After all the excitement of Spark Summit the US we thought it would be great to have a followup meetup. As usual, there will be some food and refreshments and an opportunity to network as well as some great talks! So join us for an evening of Apache Spark!

Managing the Machine Learning Lifecycle: What’s New with MLflow

Webinar

Machine learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To solve for these challenges, last June, we unveiled MLflow, an open source platform to manage the complete machine learning lifecycle. Most recently at Spark + AI Summit in San Francisco, we announced the General Availability of Managed MLflow and the upcoming release of MLflow 1.0.

AWS + Databricks Dev Day Workshop | Chicago

Regional Event

Chicago, IL

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™️, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. Tensorflow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.

AWS + Databricks Dev Day Workshop | Santa Monica

Regional Event

Santa Monica, CA

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.

Scaling Genomics Pipelines in the Cloud

Partner Event

Europe

Join Databricks at Dutch Data Science Week in Amsterdam, where Databricks Technical Director of Healthcare, Frank Austin Nothaft, will walkthrough how the Databricks Unified Analytics Platform for Genomics simplifies the end-to-end process of turning raw sequencing data into actionable insights at scale.

AWS + Databricks Dev Day Workshop (Afternoon) | Palo Alto

Regional Event

Palo Alto, CA - Afternoon Session

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.

AWS + Databricks Dev Day HLS Workshop | Boston

Regional Event

Boston, MA

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. Tensorflow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.

AWS + Databricks Dev Day Workshop (Morning) | Palo Alto

Regional Event

Palo Alto, CA - Morning Session

In this workshop, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll also learn how to use ML frameworks (i.e. TensorFlow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production on Amazon SageMaker.